What is semantic search?
Semantic search is a way of searching that looks at the meaning and intent behind a query, not just the exact words in it. It understands that "laptop for video editing under 1,500 euros" is about performance and budget, and it can return a page that never uses that exact phrase.
Traditional keyword search matches the words in a query with the words on a page. Semantic search adds an understanding of synonyms, context and the relationships between concepts. Modern systems usually do this with embeddings, which turn both the query and the content into numbers that capture meaning, and then look for the closest matches. Many search engines combine both methods.
Google has used language models such as BERT to interpret queries since 2019, and AI assistants use meaning-based retrieval to pick the passages they read before answering.
Why semantic search matters for AI visibility
In AI search, buyers type full questions and describe their situation: "which CRM works for a ten-person agency that also needs invoicing?". No page matches that sentence word for word. The sources that get picked are the ones whose meaning fits the question best, which shifts the focus from single keywords to clearly answering real needs.
It also means an assistant can understand your brand without your exact keywords, and can miss you if your pages are vague about what you do. Clear descriptions of your products, audience and use cases, plus a consistent profile of your brand across the web, make you easier to match. That ties in closely with entity optimization. It is also one reason SEO and AI visibility overlap without being the same, as our comparison of AEO, GEO and SEO explains.
How to optimize for semantic search
Start from questions, not keywords. Collect the prompts your buyers ask and make sure each important one has a clear answer on your site.
Cover the topic in depth. Explain related subtopics, such as pricing, alternatives and use cases, instead of repeating one phrase.
Be explicit. State who your product is for and what problem it solves in plain words.
Use natural variations. Synonyms and related terms help, as long as the text still reads naturally.
Add structured data. Schema markup tells machines what a page is about: a product, a company or an FAQ.
Some AI search systems, such as Google AI Mode, also split questions into many sub-queries through query fan-out, so broad coverage pays off. Trackbase tracks the prompts you choose every day across 11 AI models, so you see which questions you already win.
Examples of semantic search
A user searches "why is my sourdough flat" and gets a baking blog's article titled "5 reasons your bread doesn't rise", matched on meaning rather than wording.
An assistant asked for "a quiet bike for a daily 15 km commute" recommends a Dutch bicycle webshop's e-bike page that talks about "silent motors" and "long-distance riding".
A B2B payroll software vendor's page on "international payroll for distributed teams" is retrieved for the question "how do I pay remote staff abroad", because it covers the same need.
A travel site's internal search understands that "family holiday with toddlers in May" relates to pages about "child-friendly spring breaks".
Frequently asked questions
What is the difference between semantic search and keyword search?
Keyword search looks for pages that contain the words in the query. Semantic search looks for pages that match the meaning and intent of the query, even when the wording differs. In practice, many search engines and AI assistants combine both. For content, this means clear answers to real questions matter more than repeating an exact keyword.
Do AI assistants like ChatGPT use semantic search?
AI assistants that search the web select sources based on meaning as well as keywords, and language models themselves work with meaning rather than exact word matches. The exact systems differ per assistant and are not fully public. The practical effect is the same: pages that clearly answer the intent behind a prompt are more likely to be used.
Is keyword research still useful with semantic search?
Yes, but its role changes. Keyword research shows which topics and questions people care about and which terms they use. Instead of placing one keyword on a page many times, use it to find the questions, subtopics and variations your content should answer. Combine it with prompt research, since people phrase AI questions longer and more conversationally.